Stator winding single-phase grounding faults protective scheme based on discriminant analysis for Powerformers with selectivity. (May 2016)

• Record Type: Journal Article
• Authors: Wang, Yuanyuan; Zhou, Jiaming; Wei, Gen; Dong, Zhaoyang; Chen, Haowei
• Is Part Of: International journal of electrical power & energy systems. Volume 77(2016:May)
• Page Start: 145
• Page End: 150
• Publication Date: 2016-05
• Subjects: Powerformer -- Stator winding single-phase grounding fault -- Discriminant analysis -- Selectivity; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2015.11.037
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: The stator single phase-to-ground fault protection for parallel Powerformers. Protection scheme is based on discriminant analysis. Space relative distance is calculated to discriminate faulted powerformer. Powerformers can be detected in 100% of the winding. Abstract: In order to improve the stator winding single-phase grounding faults protective scheme for Powerformers, a protective scheme based on Mahal distance discriminant theory is proposed in this paper. By the proposed scheme, the different kinds of fault signals can be integrated. Also, a discriminant analysis model for stator winding single-phase grounding faults is established. In the proposed discriminant analysis model, four parameters including the magnitude of leakage current, the direction of leakage current, the magnitude of zero-sequence current in Powerformer terminal, and the direction of zero-sequence current in Powerformer terminal are selected as the discrimination factor. The method of back substitution estimation is introduced to verify the stability of the distance discriminant analysis model. Twenty-five groups of detected samples were analyzed based on the proposed model. The results are consistent with the measured data and show that the distance discriminant analysis model is with higher accuracy and lower mistake-discrimination ratio.